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These modes of operation are given as follows 1 Single Setup SS Alone Mode, 2 Unicast Acknowledgement Mode, 3 Broadcast Acknowledgement Mode, 4 Correction Mode starting from the sink, 5

EURASIP Journal on Wireless Communications and Networking

Volume 2009, Article ID 275694, 15 pages

doi:10.1155/2009/275694

Research Article

GRAdient Cost Establishment (GRACE) for an Energy-Aware

Routing in Wireless Sensor Networks

Noor M Khan,1Zubair Khalid,2and Ghufran Ahmed1

1 Department of Electronic Engineering, Mohammad Ali Jinnah University, Islamabad 44000, Pakistan

2 Faculty of Electronic Engineering, GIK Institute of Engineering Sciences and Technology, Topi 23640, Pakistan

Correspondence should be addressed to Ghufran Ahmed,gahmad78@gmail.com

Received 14 March 2009; Revised 27 September 2009; Accepted 8 October 2009

Recommended by Naveen Chilamkurti

In Wireless Sensor Network (WSN), the nodes have limitations in terms of energy-constraint, unreliable links, and frequent topology change In this paper we propose an energy-aware routing protocol, that outperforms the existing ones with an enhanced network lifetime and more reliable data delivery Major issues in the design of a routing strategy in wireless sensor networks are to make efficient use of energy and to increase reliability in data delivery The proposed approach reduces both energy consumption and communication-bandwidth requirements and prolongs the lifetime of the wireless sensor network Using both analysis and extensive simulations, we show that the proposed dynamic routing helps achieve the desired system performance under dynamically changing network conditions The proposed algorithm is compared with one of the best existing routing algorithms, GRAB Moreover, a modification in GRAB is proposed which not only improves its performance but also prolongs its lifetime

Copyright © 2009 Noor M Khan et al This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited

1 Introduction

1.1 Overview Advances in sensor technology, low-power

electronics, and low-power radio frequency (RF) design have

enabled the development of small, relatively inexpensive

and low-power sensors, called microsensors, which can be

wirelessly connected [1 3] to form a wireless sensor network

(WSN) The sensor nodes (or simply nodes) are usually

deployed randomly and densely in hostile environment

Depending on the environment, it may or may not be feasible

to harness energy from ambient sources, such as solar power

[4]

Sensor nodes collaborate to observe the surroundings

and send the collected information back to the sink (a node

responsible for collecting such information) in the case of

any abnormal event

WSNs find their applications in many diverse indoor

and outdoor areas including medicine, security, factory

automation, environmental monitoring, and

being used for condition-based maintenance of complex

equipment in factories In outdoor environment, these

networks can monitor natural habitats, remote ecosystems, endangered species, and emergency situations

In addition to sending the information to the sink, sensor nodes also perform complex computations for decision making within the network, either individually or in local

3000 instructions can be executed for the same cost as the transmission of one bit over 100 m An unlimited quantity

of data is generated by the physical world, but wireless telecommunication infrastructure is finite This leads to a burden on communication systems, computer networks, and human resources, which can be drastically reduced if raw data are processed at the source and the decisions

the network, communication payload may be reduced thus prolonging the network lifetime [6]

The wired networks, unlike wireless sensor networks, are not limited by energy, node failure, and lack of a centralized controller It is, therefore, easier to design and model a real-time wired network system However, due to inherent

Control centre

Sensor node Sensor field

Mobile sink (gateway)

Event area

Figure 1: Wireless Sensor Networks

problems of multihop wireless sensor networks, the design

of a routing protocol, which is not only Quality of Service

communication, is a challenging problem Applications also

set different delay requirements for the design of a routing

protocol in WSNs For instance, in surveillance applications,

authorities need to be notified sooner about high-speed

motor vehicles than slow-moving pedestrians To support

such applications, a real-time communication protocol must

adapt its behavior based on packet deadlines Hence, this

implies that due to resource constraints of WSN platforms, a

WSN protocol should introduce minimal overhead in terms

1.2 Literature Survey A general data collection problem in

a given sensor network refers to the problem of routing the

data collected by the sensor nodes to the sink as efficiently as

possible keeping in view the awareness of time and energy

However, most of the conventional routing protocols do

not consider time deadlines, energy, or congestion at the

forwarding nodes while routing a packet to its destination

a complex real-world environment If the impact of the

above-mentioned characteristics is also added to the routing

protocol designing problem, the situation is more intensified

been made by the researchers around the globe One such

effort is to study the impact of energy utilization on the

to optimal connectivity topologies for power conservation

extended for more rigorous solutions Flooding information

way of ensuring real-time packet delivery Nevertheless, this

technique has extremely poor forwarding efficiency and

results in lot of redundant transmissions, increased energy

consumption, and thus decreased network lifetime

A comparatively better approach had already been

from source to the destination over which the data are transmitted This scheme, however, results in substantial energy overhead, suffers from cache pollution, and does not consider time constraint nature of the packets Certain

find out the best route Use of GPS-capable nodes is not recommended in sensor networks due to two reasons: firstly,

it is too expensive in terms of power consumption to be used

in power-aware networks Secondly, it is subjected to failure when sensor nodes are deployed within some buildings, shades, tunnels, or caves [18]

In another real-time communication protocol, SPEED

to the destination and takes into account the presence of hot regions and congestion at forwarding nodes into its routing strategy However, it does not take into account the energy of the forwarding nodes in order to balance the node energy utilization Furthermore, the selection of region for forwarding data does not dynamically depend on the deadlines of the packets SPEED also offers low reliability since it does not transmit any redundant data packets and uses a single route for data delivery Meanwhile several other strategies were also proposed to choose an optimal path for

and so forth, but these strategies do not specifically support the stateless architecture and the energy constraints of the sensor networks

relatively better network lifetime and is fault tolerant It is also scalable and does not require geographic information

to build routing chains However it is highly complex and involves too many control overheads which in turn enhances its memory requirements in densely populated networks PAC assumes that all nodes are capable of reaching the sink node which may not be possible in randomly deployed sensor nodes

example of computationally expensive protocol and is used especially for real-time applications Since it involves very high control overhead and requires high memory, its per-formance thus degrades like SPEED in densely populated networks

uses proactive approach to build routes and thus is suited for real-time applications It routes the data reliably but dies out comparatively quicker due to energy depletion of the nodes around the hub (the node that collects the data from the network and forwards it to the base station) It also needs global identifiers which may not be feasible for large networks

delivering messages from any sensor nodes to an interested client along a minimum-cost path in a large sensor network Authors have presented a novel backoff-based cost field setup algorithm that searches for the optimal costs of all nodes to the sink with one single message overhead at each node Once the field is established, the message, carrying dynamic cost information, flows along the minimum cost path in the cost

field Each intermediate node forwards the message only if

it finds itself to be on the optimal path, based on dynamic

cost states The design does not require an intermediate node

to maintain forwarding path states explicitly It needs a few

simple operations and has an ability to scale itself to any

network size

(LURP) and Sensor Networks With Mobile Access (SeNMA)

protocol have been presented for WSNs with mobile sinks,

respectively In LURP, as the sink node moves, it only

broad-casts its location information within a local area rather than

broadcasting among the entire network The node presents

in that local area, communicating their data to the sink

dissipating lesser energy as compared to communicating the

same data from a distant location This scheme also decreases

the probability of collisions in wireless transmission One

major drawback of this protocol is that the sink broadcasta

its location information to the entire network, whenever it

goes outside the destination area So if the network is large,

the sink has to broadcast its location information to all of the

sensor nodes in the entire network, which takes a lot of time

and consumes a large portion of the available bandwidth In

SeNMA, an airplane acts as a mobile sink, which is not a

practical approach The reason is that the sensor nodes have

resource constraints like limited energy and low transmitting

ability However, a ground vehicle as a mobile sink is a

protocol, named STEER (Spatial-Temporal relation-based

distributed framework for routing data from source to the

sink In traditional approaches, a path is usually established

before the data transmitted This degrades the performance

of a routing protocol that does not work in a highly dynamic

environment In a dynamic environment, usually the path

(or set of links, or next hop nodes) chosen at an earlier time

may not work well during data transmissions after a while In

STEER, a packet is broadcast first and the node closest to the

sink among all those neighbors that receive the packet will be

chosen as the next hop relay nodes in a distributed manner

However this approach is not bandwidth-efficient as a node

broadcasts the data to each of its neighbors and thus uses

most of the bandwidth

From the above discussion, it can be concluded that the

following:

(i) the size of processor and required memory are too

large;

(ii) the bandwidth required is too high;

(iii) the protocols are not energy usage aware

These problems lead to an interesting debate on the

fun-damental limits of wireless sensor network The debate starts

with the basic question of what the maximum sustainable

throughput and the maximum lifetime of a network are

The answers to these and similar other questions are of great

importance to both the theoretical and practical aspects of

wireless sensor networking research

As discussed earlier, a lot of work has been done in addressing the above issues in WSNs However every listed piece of work either discusses only one issue from the above two issues and ignores the other one completely or gives lesser importance to one or both of them Our research thus finds its directions to the theoretical underpinnings

that can ensure sustainable higher throughput in WSN with prolonged lifetime In addition, the aim of this work is to find

minimal overhead

Organization of the rest of the paper is as follows Section 2discusses the proposed strategy, GRACE, in detail Section 3 presents various modes of operation involved

in updating procedure of status information in routing

considering various performance metrics, which are usually used to evaluate the performance of routing strategy in

concludes the paper and discusses the future work

2 Proposed Routing Strategy—GRAdient Cost Field Establishment (GRACE)

The drawbacks and shortcomings of the routing strategies

imple-menting better broadcast routing approaches The resulting improved routing strategy thus presents good results and outperforms the previous routing approaches published in literature so far

2.1 GRACE System Model 2.1.1 Model Assumptions We randomly deploy a large

number of sensor nodes in a monitoring area, which sense the data and send it to the control center via stationary sink

We make the following assumptions in the present study (i) To simplify the energy analysis, the time for sending

a certain amount of data is assumed to be the same as the time for receiving the same amount of data (ii) The distance from the different nodes to the sink is ignored as we are dealing with the number of hops instead of propagation delay which is usually based

on the physical distance from source to the sink (iii) All sensor nodes are assumed to be homogeneous; therefore the energy consumption for sensing is the same to each sensor node

2.1.2 Stochastic Model As we know that the radio pattern

is largely random, there are certain other factors which are also random; but once we pick a particular value of

a parameter for an experiment, it becomes deterministic For example, the value of transmission power can be a uniformly distributed random variable and can be varied from [max, min], but in order to start an experiment we pick a particular power value This value remains constant till

the end of the experiment Hence, for an entire process, the

value of transmission power can be selected randomly from

its domain; therefore the process is called as random process

or stochastic process

We can apply same procedure to the weather

condi-tions and other environmental factors After completing

process becomes a random process and we can apply

random variables which combine to form a whole random

process It is also called a set of samples or a set of

samplesX(t, S1),X(t, S2), , X(t, S n) from each of different

sensor nodes S1,S2,S3, , S n after a specific time interval

t1,t2,t3, , t n The collection of data points from different

of these random variables is a probability mass function

(pmf) or a probability density function (pdf) Therefore if

there are n index random variables: x1,x2,x3, , x n, then

f Xn(x) In addition, there is a joint pdf corresponding to

all of these pdfs In other words, in order to represent

the entire random process which consists of a set of index

random variablesx1,x2,x3, , x n, we should have a joint pdf

f(x1 ,x2 ,x3 , ,x n) which can represent or characterize the entire

random process We can get this joint pdf by summing up

each of these individual pdfs

The joint probability density function is given by

f x1 ,x2 ,x3 , ,x n = f x(t)(x). (1) The mean, variance, autocorrelation, autocovariance, and

can be obtained from (2), (3), (4), (5), and (6), respectively:

(i) Mean:

m x(t) =

+∞

−∞ f x(t) x dx, (2) (ii) Variance:

var[x(t)] =

+∞

−∞



x − m2x(t)

f x(t) x dx,

var[x(t)] = E

x2(t)

− E2[x(t)],

(3)

(iii) Auto Correlation:

R x(t1 , 2 )= E[x(t1)x(t2)]= E[x1x2], (4)

(iv) Auto Covariance:

C x(t1 , 2 )= R x(t1 , 2 )− m x(t1 )m x(t2 ), (5)

ρ x(t1 , 1 )= C x(t1 , 2 )

C x(t1 , 1 )



C x(t2 , 2 )

X(t, S1 )

X(t, S2 )

X(t, S3 )

X(t, S4 )

X(t, S5 )

X(t, S n)

t

t

t

t

t

t X(t n S) = X(t n)= X n

Time:

RV:

PDF:

t1

x1

f x1 (x)

t2

x2

f x2 (x)

t3

x3

f x3 (x)

t n

x n

f x n(x)

· · ·

· · ·

· · ·

Figure 2: Random Process

We are dealing with an event-based WSN system where the sensor nodes activate whenever an event occurs These events occur according to a random process with a rate denoted as

λ Hence we collect the data X each time an event occurs.

LetX(t) be the total data collected till time t, as shown in

Figure 3:

X(t) =

n



i =0

x(i). (7)

The probability that the total data collected till timet, X(t),

equal toj is given by

P

X(t) = j

=

∞



n =0

P

X(t) = j

N(t) = n P[N(t) = x] (8)

HereX nis a poison process, and therefore

∞



n =0

P

X(t) = j

N(t) = n = n j

j!exp

− n (9)

P

X(t) = j

=

∞



n =0

n j

j!exp

− n(λt) n n! exp

− λt (10)

2.1.3 GRACE Parameters Each sensor node is defined by

a infovalue pair These infovalue pairs have already been

Total number of events:N(t) = n

· · ·

PMF=(λt) n

n! e

−λt

Figure 3: Poisson Process

again briefly

Energy of Node, I E,i In order to increase the lifetime

of WSN, low-energy nodes are avoided in routing This is

achieved by maintaining the following attribute for each

node:

I E,i = P i0

P i

whereP iis the remaining battery power andP0

i is the starting battery power From the above formula, we can conclude

that we should avoid those paths which contain nodes having

high value ofI E,i

Link Cost, I L The proposed strategy uses link costs that

reflect the communication energy consumption rates at the

two end nodes The aim of the strategy is to maximize the

lifetime of the network by carefully defining link cost as a

function of receiving and transmission power using that link

The transmission-value is set initially same for all the nodes

follows:

I L,u − v = P t,u

P r,v

representI L,u − vasI Lfrom now onward

that there exist more chances of packet drop and more

transmission energy would be required to overcome the

hindrances of the path So we can conclude that we should

avoid such links that have higher values ofI L

2.2 Phases of GRACE

2.2.1 Setup Phase Algorithm Most of the WSNs routing

strategies are data-centric In data-centric strategies, sink

sends interest packets to the area in the sensor field where

it wants to collect the data However in our strategy, which

is more generalized as compared to the above mentioned

approach, the sink initiates the setup phase for the entire

WSN In the setup phase, a cost propagates throughout

the sensor field This cost field is established using the

advertisement packet

Sink

i

k

I L,k−sink

I L, j−k

I L,k−L

I L,i−k

I L,i−L

I L,i− j

Figure 4: Cost Field Establishment

(i) LetC i-Sinkbe the cost of the path which heads to the

(ii) LetC i jbe the cost of the path which heads to the sink viajth node from the ith node.

(iii) LetA ibe the advertisement packet broadcasted byith

node to its immediate neighbors

The cost field propagation is better understandable by

fields and advertisement packets as follows,

A j = C j-Sink+I E, j,

A k = C k-Sink+I E,k,

A l = C l-Sink+I E,l,

C i j = A j+I L,i − j,

C ik = A k+I L,i − k,

C il = A l+I L,i − l,

C i-Sink =min

C i j,C ik,C il



.

(13)

Initially Cnode-Sink is set to infinite for all the nodes

in the sensor field The sink initiates the setup phase by broadcasting the advertisement packet containing the cost

receives the advertisement message with the cost, it stores the cost in its routing table Then it calculates the link costI L,node-Sink, as described in (12) Thus, a node’s routing

table contains cost C received from each of its immediate

neighbors along with the neighbors’ id Now, the receiving node (sayi) picks the smallest C value from its routing table,

adds its ownI E,icost in it, and broadcasts this final valueA i

to all of its immediate neighbors Also, the receiving node considers the smallest value node as the relay node to send data back to the sink The similar algorithm is running on other nodes and this process continues till the last node of the sensor field Once the setup phase is completed, the steady-state phase is performed to find the best path

D

G

H

I

J

E F

4

2 1

1 1 1 2

3

4 1

1 3

Figure 5: Example Scenario

2.2.2 Steady-State Phase Algorithm After the completion

of the setup phase, the source node sends the data to that

particular node which has the smallest cost C value in its

routing table The receiver then forwards the data to that

node having the smallest cost C value in its routing table and

the same process continues till the data reach to the sink In

order to update the status information of sensor nodes, we

propose different modes of operations that will be discussed

in detail inSection 3

2.3 An Example Scenario of the Proposed Strategy The setup

and steady-state phases can be better understandable if

we take an example Let us take an example network as

are calculated using (11) and (12), respectively First the

SINK node broadcasts the advertisement message to nodes

B, D, and J This advertisement message contains the cost

their respective link costsI L,B-Sink,I L,D-Sink, andI L,J-Sink, and

then add their link costs to ASink to form C B-Sink,C D-Sink,

and C J-Sink, respectively Nodes B, D, and J store these

information in their routing tables, as shown inTable 1 After

a certain period of time, which depends on these costs as

discussed in [27], the nodes select the minimum costC x-Sink

from their routing tables, add their own energy costI Ein it,

and broadcast it to all of their immediate neighbors (In the

and E Node D broadcasts its advertisement A D to nodes

A, C, and G Node J broadcasts its advertisement A J to nodes

A and I) The same procedure also runs at nodes G, C, E,

andI This process goes on one after the other according to

their intervals, till the last node of the sensor field establishes

its routing table After the setup phase, steady-state phase

for the node in its routing table which has the smallest cost

F Same decisions for forwarding data are made on other

nodes In this way data reach the sink with minimal routing

overhead

3 Modes of Operation for Updating

Status Information

We propose various modes of operation for updating

status information of the sensor nodes in the WSNs The

performance of any routing strategy depends on the use of any particular mode In this section, we present the behavior

of our proposed routing strategy under the operation of these modes These modes of operation are given as follows (1) Single Setup (SS) Alone Mode,

(2) Unicast Acknowledgement Mode, (3) Broadcast Acknowledgement Mode, (4) Correction Mode (starting from the sink), (5) Correction Mode (starting from the intermediate node)

The setup phase will be run at start and information update will be made according to the operation of these modes The plots showing the behavior of these modes on the performance of the network would consequently be used for choosing the best mode of operation for the information update procedure

3.1 Single Setup (SS) Alone Mode In this mode of operation,

the setup phase runs only once at the startup Thus later on using this mode, there is no mechanism to update the status information of sensor nodes This leads to the continuous usage of a routing path till any of the node in the path dies

example to illustrate various modes of operations

3.2 Unicast Acknowledgement Mode Since every node has

cost factors of its neighbor nodes, it selects node for routing data that has minimum cost Later on, this cost factor is updated in such a way that the receiving node sends an acknowledgement to the sender whenever it receives the data This acknowledgement comprises of one extra byte, showing the current minimum cost factor of the receiver node Thus, the updates propagate in the sensor field by

shows the Unicast Acknowledgement Mode

3.3 Broadcast Acknowledgement Mode One major drawback

of the acknowledgement phase is that only the sender knows about the updated status information of the receiving node In order to prevent from it, the receiving node can broadcast the acknowledgement along with its updated status information to all of its immediate neighbors In this way, a node can inform all of its neighbors about its

Acknowledgement Mode

3.4 Correction Mode (Starting from the Sink) Whenever

a node sends data packet to another node, it keeps the packet ID in its buffer Similarly, every node gets a list of all the packet IDs it receives Whenever a packet reaches the sink, sink sends the acknowledgment to the node from which it receives the packet That node then broadcasts the acknowledgement containing its updated status information

to all of its neighbors along with data packet IDs The packet

ID will help recognize the corresponding node among the

Table 1: Energy Levels of Nodes at some time after the deployment of the Network.

Table 2: Cost Fields

ith Node Neighborjth Node A j I L,i− j C i j C i-Sink I E,i A i

neighbors which took part in carrying that packet This

process will continue till the source node, which originated

the data packet, get the corrected cost of the path used in

carrying its data Storing packet IDs gives an extra burden to

the node memory In order to minimize this burden, node

will use a specified memory for packet ID storing on FIFO

basis Consequently, in case of congestion in a particular

region of the network, node will lose the packet ID from its

memory and hence will stop broadcasting for not allowing

Correction Mode (Starting from the sink)

3.5 Correction Mode (Starting from the Intermediate Node).

Sometimes the packet is lost or dropped at some

interme-diate node In this case the correction mode will not be

initiated as the packet is not reached at the sink Therefore

there must be a mechanism which initiates the correction

operation at any intermediate node, so that the updated

cost field is propagated along the entire path Correction

operation starting from the intermediate node is a solution

(Starting from the intermediate node)

Table 3: Parametric values used in Simulations

4 Results and Discussion

4.1 Simulation Setup To investigate the performance and

the scalability of the proposed protocol, we generate a sensor network comprising of 100 nodes and carry out extensive simulations in Matlab 6.0 in order to validate the proposed routing strategy under different modes of operation Our sensor field’s dimension is 0.0025 Kilometer Square The numerical values chosen for our simulations can be seen in Table 3

Sink 2 6 9 18 15 Source 11

16 19

10

4

1

3 5 8

12

17 14 13

20 7

0

5

10

15

20

25

30

35

40

45

50

Distance (m)

Data packet

Unicast acknowledgment

Figure 6: Unicast Acknowledgment Mode

Sink 2 6 9

18 15 Source 11

16 19

10

4

1

3 5 8

12

17 14 13

20 7

0

5

10

15

20

25

30

35

40

45

50

Distance (m)

Data packet

B.C from node

Figure 7: Broadcast (B.C) Acknowledgment Mode

4.2 Performance Metrics A set of performance metrics is

used for evaluating the performance of the proposed strategy

One point that should be kept in mind is the degree

of goodness or badness of the results It is clear that it

depends on the working life of the network A network

having only one established path from the source to the

sink is much better than the network that has got large

number of disconnected nodes scattered in the field This

takes us to the strategy that utilizes the network nodes on a

uniform balanced manner Another criterion that promises

the reliability and useability of the network is preventing

the nodes from dying till a large number of nodes die out

collectively The collective death of a large number of nodes

will ensure a reliable data delivery and network operation for

a specified time This time would thus give us a prediction

about the safe operation of the network The use of network

beyond this time would make its operation unreliable and

unpredictable The figures show the result obtained under

various scenarios and modes of operation

Sink 2 6 9 18 15 Source 11

16 19 10 4 1

3 5 8

12

17 14 13

20 7

0 5 10 15 20 25 30 35 40 45 50

Distance (m)

Data packet

(a) Sink 2 6 9

18 15 Source 11

16 19 10 4 1

3 5 8

12

17 14 13

20 7

0 5 10 15 20 25 30 35 40 45 50

Distance (m)

B.C from sink

(b)

Figure 8: Correction Mode (Starting from the sink) (a) Data Packets (b) Acknowledgment Packets

4.2.1 Network Lifetime (in Terms of Node Failures, f ) It

number of alive nodes is plotted against simulation time units It can be seen that the correction mode from intermediate node has the lowest working life while the broadcast acknowledgement mode has the highest working lifetime, thus keeping a large number of nodes alive with high data rate and reliable data delivery The reason of this difference in results is that setup phase with the broadcast acknowledgement uses the nodes evenly in terms of energy utilization, while the other approaches like GRAB [27] do not ensure a balance utilization of nodes

InFigure 11, we draw a bar graphs of the node failure,

f (in percentage) versus time elapsed It is also clear

from that when first node dies, single setup with unicast acknowledgement mode has longer time elapsed, while the

elapsed This is due to the fact that in case of single setup mode, which is based upon the initial nodes’ status

Intermediate node Sink

2 6 9 18 15 Source 11

16 19

10

4

1

3 5 8

12

17 14 13 20 7

0

5

10

15

20

25

30

35

40

45

50

Distance (m)

Data packet

(a)

Intermediate node Sink

2 6 9

18 15

Source 11

16 19

10

4

1

3 5 8

12

17 14 13

20 7

0

5

10

15

20

25

30

35

40

45

50

Distance (m)

Broadcast acknowledgment

(b)

Figure 9: Correction Mode (Starting from the intermediate node)

(a) Data Packets (b) Acknowledgment Packets

information, it continuously uses a path till any of the nodes

in the path dies While in case of GRAB [27], the setup phase

will not run till the occurrence of any event

4.2.2 Network Energy Left, e It shows the amount of energy

plots of the network energy versus simulation time From the

figure, it is clear that use of single setup mode outperforms

the others if energy consumption is considered This is due

to the fact that the setup phase runs only at the startup and

no acknowledgment and correction is done at later times

Although this mode is good in the energy consumption sense

but as a result of not using acknowledgement and correction,

it loses data reliability as compared to other nodes

4.2.3 Data Reliability, μ It shows the success ratio of the data

packets, that is, the number of data packets received by the

0 50 100 150 200 250

Timet (units)

Single setup (SS)

SS with unicast acknowledgement

SS with broadcast acknowledgement

SS with correction from sink

SS with correction from intermediate node

SS with hybrid correction + acknowledgement GRAB, event based setup initialization (Ye et al.)

Figure 10: Network Lifetime: SS Alone, SS with Unicast, SS with Broadcast, SS with Correction from Sink, SS with Correction from Intermediate Node, SS with Hybrid Mode and GRAB, an event-based setup initialization (Ye et al [27])

0 100 200 300 400 500 600

Node failure,f (% age)

Single setup (SS)

SS with unicast acknowledgement

SS with broadcast acknowledgement

SS with correction from sink

SS with correction from intermediate node

SS with hybrid correction + acknowledgement GRAB, event based setup initialization (Ye et al.)

Figure 11: Node Failure in Percentage: SS Alone, SS with Unicast,

SS with Broadcast, SS with Correction from Sink, SS with Correc-tion from Intermediate Node, SS with Hybrid Mode and GRAB, an event-based setup initialization (Ye et al [27])

20

30

40

50

60

70

80

90

100

Timet (units)

Single setup (SS)

SS with unicast acknowledgement

SS with broadcast acknowledgement

SS with correction from sink

SS with correction from intermediate node

SS with hybrid correction + acknowledgement

GRAB, event based setup initialization (Ye et al.)

Figure 12: Network Energy Left: SS Alone, SS with Unicast, SS with

Broadcast, SS with Correction from Sink, SS with Correction from

Intermediate Node, SS with Hybrid Mode and GRAB, an

event-based setup initialization (Ye et al [27])

sink out of the total number of data packets generated by the

source InFigure 13one aspect of data reliability comparison

is shown, where the plots represent the percentage data

delivery with respect to simulation time It is clear from the

figure that the hybrid approach and the single setup with

broadcast acknowledgement have high data reliability This

is due to the fact that the status information of the sensor

nodes is updated frequently, in these modes of operation

Another aspect of data reliability comparison is shown in

Figure 14, where the plots show interval-based data delivered

to the sink after a specified time interval (e.g., after each 100

seconds in our case); we note down the number of data

pack-ets received at the sink It can be noted from the plots that

initially the single setup with broadcast acknowledgement

mode has the highest percentage of delivered packets to the

sink but cannot keep its pace at later times and degrades its

performance due to bulk node failures

Discussing the last aspect of data-delivery performance

comparison, the packet received by the sink have been

that the single setup with broadcast acknowledgement mode

has large number of packets received The reason is obvious

that in the single setup with broadcast acknowledgement

mode status information of the sensor nodes is updated

frequently and thus nodes are evenly utilized

4.2.4 Collective Performance Metric, β = (f × μ × e).

reflect the network energy left, reliability, and the node

20 30 40 50 60 70 80 90 100

Timet (units)

Single setup (SS)

SS with unicast acknowledgement

SS with broadcast acknowledgement

SS with correction from sink

SS with correction from intermediate node

SS with hybrid correction + acknowledgement GRAB, event based setup initialization (Ye et al.)

Figure 13: Data Delivery in Percentage: SS Alone, SS with Unicast, SS with Broadcast, SS with Correction from Sink, SS with Correction from Intermediate Node, SS with Hybrid Mode and GRAB, an event-based setup initialization (Ye et al [27])

μint

0 10 20 30 40 50 60 70 80 90 100

Timet (units)

Single setup (SS)

SS with unicast acknowledgement

SS with broadcast acknowledgement

SS with correction from sink

SS with correction from intermediate node

SS with hybrid correction + acknowledgement GRAB, event based setup initialization (Ye et al.)

Figure 14: Interval-based Data Delivery in Percentage: SS Alone,

SS with Unicast, SS with Broadcast, SS with Correction from Sink,

SS with Correction from Intermediate Node, SS with Hybrid Mode and GRAB, an event-based setup initialization (Ye et al [27])